Performance Limitations for Sparse Matrix-Vector Multiplications on Current Multi-Core Environments

Gerald Schubert (), Georg Hager () and Holger Fehske ()
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Gerald Schubert: Friedrich-Alexander Universität Erlangen-Nürnberg, Regionales Rechenzentrum Erlangen
Georg Hager: Friedrich-Alexander Universität Erlangen-Nürnberg, Regionales Rechenzentrum Erlangen
Holger Fehske: Ernst-Moritz-Arndt-Universität Greifswald, Institut für Physik

A chapter in High Performance Computing in Science and Engineering, Garching/Munich 2009, 2010, pp 13-26 from Springer

Abstract: Abstract The increasing importance of multi-core processors calls for a reevaluation of established numerical algorithms in view of their ability to profit from this new hardware concept. In order to optimize the existent algorithms, a detailed knowledge of the different performance-limiting factors is mandatory. In this contribution we investigate sparse matrix-vector multiplications, which are the dominant operation in many sparse eigenvalue solvers. Two conceptually different storage schemes and computational kernels have been conceived in the past to target cache-based and vector architectures, respectively: compressed row and jagged diagonal storage. Starting from a series of microbenchmarks to single out performance limitations, we apply the gained insight to optimize sparse MVM implementations, reviewing serial and OpenMP-parallel performance on state-of-the-art multi-core systems.

Keywords: Access Pattern; Memory Bandwidth; Cache Line; Storage Scheme; Sparsity Pattern (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-13872-0_2

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DOI: 10.1007/978-3-642-13872-0_2

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